脳にインスパイアされたコンピューティングのためのシステム階層
Youhui Zhang1,2,3, Peng Qu4,5,6, Yu Ji4,5,6
1Department of Computer Science and Technology, Tsinghua University, Beijing, China. zyh02@tsinghua.edu.cn.
Nature
|October 15, 2020
まとめ
ニューロモルフィック・コンピューティングには 普遍的なシステム・ヒエラルキーがない. この研究は,脳にインスパイアされたAI開発のためのプログラミング言語の移植性とハードウェアの互換性を確保するための"ニューロモルフィック完全性"とシステム階層を導入します.
科学分野:
- コンピュータ工学
- 人工知能
- 神経科学
背景:
- ニューロモルフィック・コンピューティングは 脳からインスピレーションを得て 次世代のコンピュータ工学と 人工知能の可能性を秘めています
- 現在の脳にインスパイアされたコンピューティングは,一般的なシステム階層と完全性の理解が欠如しており,ソフトウェアとハードウェアの互換性と開発の生産性を阻害しています.
研究 の 目的:
- 脳にインスパイアされたコンピューティングのための"ニューロモルフィック完全性"と対応するシステム階層を提案する.
- 現在のニューロモルフィックシステムにおけるプログラミング言語の移植性とハードウェアの互換性の欠如に対処する.
主な方法:
- ハードウェアの完全性要件を緩和した"ニューロモルフィック完全性"が導入されました.
- チューリング完全ソフトウェア抽象化モデルと汎用的な抽象ニューロモルフィックアーキテクチャを持つシステム階層を開発した.
- 様々なニューロモルフィックハードウェアプラットフォームでプログラムを実行するためのツールチェーンのソフトウェアを実装します.
主要な成果:
- ニューロモルフィックな完全なハードウェアのための統一したプログラム表現と変換を可能にするシステム階層を確立した.
- プログラミング言語の移植性,ハードウェアの完全性,およびコンパイルの実行可能性が実証されています.
- ニューロモルフィックの完成度によって新しいシステム設計の次元を導入しました.
結論:
- 提案されたシステム階層と神経形的な完全性は,脳にインスパイアされたコンピューティングの効率的かつ互換的な進歩を促進します.
- この枠組みは,人工知能を含む様々なアプリケーションの開発を加速させる見込みです.
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